Residual Sum of Squares (RSS)

The residual sum of squares (RSS) is a measure of how well the regression line fits the data points in a scatter plot. It is calculated by squaring the difference between each data point and the value predicted by the regression line, and then summing these squared differences. The smaller the RSS, the better the regression line fits the data.

The RSS can be used to compare different regression models and to select the model that best fits the data. It can also be used to test the significance of a regression model. If the RSS is significantly smaller than the total sum of squares (TSS), then the regression model is statistically significant.

The RSS is a useful tool for understanding how well a regression model fits the data. However, it is important to note that the RSS does not take into account the variability of the data. This means that a regression model with a small RSS may not be the best model if the data is very variable.

In addition, the RSS is not a measure of the accuracy of the regression model. It only measures how well the regression line fits the data. A regression model with a small RSS may not be very accurate if the data is not normally distributed.

The residual sum of squares (RSS) is a useful tool for understanding how well a regression model fits the data. However, it is important to be aware of its limitations before using it to make decisions.